We introduce distributed machine learning methods and provide two machine learning models applications. Using historical wind farm measurements from SCADA data, we develop a non-physical statistical model of the system to predict component failures. A supervised learning algorithm will be utilized to analyze the wealth of operational data recognizing conditional state patterns. This information coupled with additional expert knowledge of turbine component limits forms the basis of a statistical model to identify future failures. Also, a machine learning model predicting turbine performance over a range of climatic conditions is presented. An unsupervised learning algorithm has been trained on power performance data from a collection of independent tests. Key turbine performance drivers are identified. Model validations of the turbine performance model show improved predictive accuracy over traditional matrix classifier methods.

Presenter: Michael Wilkinson, Service Line Leader, Asset Operations & Management, DNV GL - Energy
Date/time: Thursday 14 April 2016, 15:00-17:00
Session 3: Using data for advanced performance modelling
Presentation: Use of Higher Frequency data for Turbine Performance Optimisation
Abstract: Analysis of 10-minute SCADA data is now established as an important tool for the optimisation of operational wind turbine performance. Many SCADA systems also generate higher frequency data at around 1 Hz but due to the high volume these data have historically been ignored and archived or even discarded.

However, with increasing computing power, and decreasing storage costs, analysis of these data is becoming increasingly viable. The authors will demonstrate, through the use of case studies, the benefit of looking in more detail at these higher frequency data. Insight can be gained into the performance of the turbine: for example the nacelle anemometer wind speed signals can be used to measure turbulence and assess how high and low turbulence affects real-world turbine performance. Through the analysis of the correlation between signals such as wind speed, generator speed, pitch angle and/or electrical power, it is possible to observe high level controller behaviour such as: closed-loop pitch and torque controller relationships; above- and below-rated power transitions; and supervisory controller actions.

SCADA-based condition monitoring with 10-minute data, although valuable, is often limited to temperature analysis. Using 1 second data unlocks a range of additional analysis possibilities, for example monitoring first tower frequency deviation over time to assess structural health and estimating fatigue loading which provides valuable data for turbine life extension.

With 10-minute data, the effectiveness of a turbine’s yaw strategy can be difficult to assess. However with higher frequency data it can be demonstrated that more frequent yaw-manoeuvres leads to increased energy capture.

Presenter: Keir Harman, Head of Asset Operations and Management, DNV GL – Energy
Date/time: Friday 15 April 2016, 11:45-13:30
Session 5: Innovation in operations
Presentation: Infrastructures to enable wind and solar projects to actively participate in balancing European grid systems
Abstract: With the rapid increase of renewable energy penetrating our grid systems there is a strong demand for all projects to operate more like conventional generating plants, in a more visible, predictable and controllable way. This talk will focus on the need and opportunities on European grid systems for renewables to offer balancing services and other ancillary services. For example, rapid curtailment, reactive power export control, fast frequency response and even spinning reserve.

It will look at how such services must be underpinned with accurate forecasting, reliable data and real-time communications with the system operator. A particular focus on the capability of wind farms and how they can operate in a dynamic way to offer these services will be presented. The potential for aggregation of projects and technologies including how they may be coupled with storage is also considered. One conclusion being that participation in this way will open up a revenue stream to project owners that counters the current diminishing income from government support mechanisms as renewable markets mature.

Presenter: Lars Landberg, Director, Strategic Research and Innovation, DNV GL – Energy
Date/time: Friday 15 April 2016, 11:45-13:30
Session 5: Innovation in operations
Presentation: The drones are coming!
Abstract: Drones have clearly become the “new black” within the application of robotics to all kinds of problems. Wind Energy is no exception, and in this presentation a quick introduction to state-of-the-art drones will be given, and after that the application of drones to inspect wind turbines and wind farm sites (potential and operating) will be discussed.

Real life examples of both applications will be given, and issues like accuracy of positioning, quality of images, treatment of the resulting data will be discussed. Also examples of practical issues will be given, like battery life, insurance, aviation regulations and so on. A brief discussion on the economics around the use of drones will also be given.

At the end some perspectives on the future application of drones and robotics will be given, these include flying BVLoS (Beyond Visual Line of Sight), autonomous operation and the potential of using AI in connection with robotics in wind energy.

We introduce distributed machine learning methods and provide two machine learning models applications. Using historical wind farm measurements from SCADA data, we develop a non-physical statistical model of the system to predict component failures. A supervised learning algorithm will be utilized to analyze the wealth of operational data recognizing conditional state patterns. This information coupled with additional expert knowledge of turbine component limits forms the basis of a statistical model to identify future failures. Also, a machine learning model predicting turbine performance over a range of climatic conditions is presented. An unsupervised learning algorithm has been trained on power performance data from a collection of independent tests. Key turbine performance drivers are identified. Model validations of the turbine performance model show improved predictive accuracy over traditional matrix classifier methods.

Presenter: Michael Wilkinson, Service Line Leader, Asset Operations & Management, DNV GL - Energy
Date/time: Thursday 14 April 2016, 15:00-17:00
Session 3: Using data for advanced performance modelling
Presentation: Use of Higher Frequency data for Turbine Performance Optimisation
Abstract: Analysis of 10-minute SCADA data is now established as an important tool for the optimisation of operational wind turbine performance. Many SCADA systems also generate higher frequency data at around 1 Hz but due to the high volume these data have historically been ignored and archived or even discarded.

However, with increasing computing power, and decreasing storage costs, analysis of these data is becoming increasingly viable. The authors will demonstrate, through the use of case studies, the benefit of looking in more detail at these higher frequency data. Insight can be gained into the performance of the turbine: for example the nacelle anemometer wind speed signals can be used to measure turbulence and assess how high and low turbulence affects real-world turbine performance. Through the analysis of the correlation between signals such as wind speed, generator speed, pitch angle and/or electrical power, it is possible to observe high level controller behaviour such as: closed-loop pitch and torque controller relationships; above- and below-rated power transitions; and supervisory controller actions.

SCADA-based condition monitoring with 10-minute data, although valuable, is often limited to temperature analysis. Using 1 second data unlocks a range of additional analysis possibilities, for example monitoring first tower frequency deviation over time to assess structural health and estimating fatigue loading which provides valuable data for turbine life extension.

With 10-minute data, the effectiveness of a turbine’s yaw strategy can be difficult to assess. However with higher frequency data it can be demonstrated that more frequent yaw-manoeuvres leads to increased energy capture.

Presenter: Keir Harman, Head of Asset Operations and Management, DNV GL – Energy
Date/time: Friday 15 April 2016, 11:45-13:30
Session 5: Innovation in operations
Presentation: Infrastructures to enable wind and solar projects to actively participate in balancing European grid systems
Abstract: With the rapid increase of renewable energy penetrating our grid systems there is a strong demand for all projects to operate more like conventional generating plants, in a more visible, predictable and controllable way. This talk will focus on the need and opportunities on European grid systems for renewables to offer balancing services and other ancillary services. For example, rapid curtailment, reactive power export control, fast frequency response and even spinning reserve.

It will look at how such services must be underpinned with accurate forecasting, reliable data and real-time communications with the system operator. A particular focus on the capability of wind farms and how they can operate in a dynamic way to offer these services will be presented. The potential for aggregation of projects and technologies including how they may be coupled with storage is also considered. One conclusion being that participation in this way will open up a revenue stream to project owners that counters the current diminishing income from government support mechanisms as renewable markets mature.

Presenter: Lars Landberg, Director, Strategic Research and Innovation, DNV GL – Energy
Date/time: Friday 15 April 2016, 11:45-13:30
Session 5: Innovation in operations
Presentation: The drones are coming!
Abstract: Drones have clearly become the “new black” within the application of robotics to all kinds of problems. Wind Energy is no exception, and in this presentation a quick introduction to state-of-the-art drones will be given, and after that the application of drones to inspect wind turbines and wind farm sites (potential and operating) will be discussed.

Real life examples of both applications will be given, and issues like accuracy of positioning, quality of images, treatment of the resulting data will be discussed. Also examples of practical issues will be given, like battery life, insurance, aviation regulations and so on. A brief discussion on the economics around the use of drones will also be given.

At the end some perspectives on the future application of drones and robotics will be given, these include flying BVLoS (Beyond Visual Line of Sight), autonomous operation and the potential of using AI in connection with robotics in wind energy.